Deep learning–based prediction of neoadjuvant therapy response in HER2-positive breast cancer through histopathology images of core biopsies: A multicenter study.

e12597 Background: Early-stage HER2-positive breast cancer patients who achieve pathological complete response (pCR) after neoadjuvant therapy generally experience favorable survival outcomes. However, only 40-60% of HER2-positive patients achieve pCR. This study developed a deep learning model using histopathology images to predict neoadjuvant therapy response in HER2-positive breast cancer across different regimens, aiming to guide personalized treatment choices. Methods: In this multi-centered retrospective study, we recruited 402 HER2-positive breast cancer patients from four hospitals: 320 patients from two centers were divided into the discovery cohort (n=223) and internal testing cohort (n=97), while 82 patients from the other two centers served as external validation cohorts (n=21 and n=61). We collected pre-treatment H&E-stained histopathology images, clinical information, neoadjuvant treatment regimens, and established a pCR prediction model, named Histomics-Clinic-Regimen Integrated pCR Prediction Model (HIPPM), which integrates CAMEL2 and FCNN approaches. Model performance was evaluated using area under the curve (AUC), sensitivity (SE), specificity (SP), and accuracy. Additionally, we utilized the weight matrix from the first fully connected layer to assess the relative importance of each clinical variable. Results: The initial model based solely on histopathology images demonstrated the following performance: internal testing cohort (AUC 0.71, SE 0.73, SP 0.70, accuracy 70.6%), external validation cohort 1 (AUC 0.95, SE 1.00, SP 0.94, accuracy 95.2%), and validation cohort 2 (AUC 0.61, SE 0.37, SP 0.85, accuracy 53.3%). After integrating clinical and regimen data into the HIPPM model, performance improved significantly: internal testing cohort (AUC 0.85, SE 0.73, SP 0.91, accuracy 88.2%), external validation cohort 1 (AUC 0.94, SE 1.00, SP 0.94, accuracy 95.2%), and validation cohort 2 (AUC 0.74, SE 0.82, SP 0.67, accuracy 73.3%). Model analysis revealed that T stage, HER2 expression, N stage, and targeted therapy regimen had the highest weights, while Ki67 expression, age, ER expression, and chemotherapy regimen had lower weights. Based on HIPPM, we developed a predictive tool that allows patients to upload biopsy images and clinical information pre-treatment to predict pCR probability for 12 virtual regimens and recommend the optimal drug combination. Conclusions: HIPPM effectively predicts neoadjuvant therapy response in HER2-positive breast cancer, aiding in selecting optimal targeted and chemotherapeutic regimens. This model lays the foundation for patient screening and personalized treatment strategies.